The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020–50: a health-augmented macroeconomic modelling study

Summary Background Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide and imposes a substantial economic burden. Gaining a thorough understanding of the economic implications of COPD is an important prerequisite for sound, evidence-based policy making. We aimed to estimate the macroeconomic burden of COPD for each country and establish its distribution across world regions. Methods In this health-augmented macroeconomic modelling study we estimated the macroeconomic burden of COPD for 204 countries and territories over the period 2020–50. The model accounted for (1) the effect of COPD mortality and morbidity on labour supply, (2) age and sex specific differences in education and work experience among those affected by COPD, and (3) the impact of COPD treatment costs on physical capital accumulation. We obtained data from various public sources including the Global Burden of Disease Study 2019, the World Bank database, and the literature. The macroeconomic burden of COPD was assessed by comparing gross domestic product (GDP) between a scenario projecting disease prevalence based on current estimates and a counterfactual scenario with zero COPD prevalence from 2020 to 2050. Findings Our findings suggest that COPD will cost the world economy INT$4·326 trillion (uncertainty interval 3·327–5·516; at constant 2017 prices) in 2020–50. This economic effect is equivalent to a yearly tax of 0·111% (0·085–0·141) on global GDP. China and the USA face the largest economic burdens from COPD, accounting for INT$1·363 trillion (uncertainty interval 1·034–1·801) and INT$1·037 trillion (0·868–1·175), respectively. Interpretation The macroeconomic burden of COPD is large and unequally distributed across countries, world regions, and income levels. Our study stresses the urgent need to invest in global efforts to curb the health and economic burdens of COPD. Investments in effective interventions against COPD do not represent a burden but could instead provide substantial economic returns in the foreseeable future. Funding Alexander von Humboldt Foundation, National Natural Science Foundation of China, CAMS Innovation Fund for Medical Science, Chinese Academy of Engineering project, Chinese Academy of Medical Sciences and Peking Union Medical College project, and Horizon Europe. Translations For the Chinese and German translations of the abstract see Supplementary Materials section.

In this supporting information appendix to the paper "The global economic burden of chronic obstructive pulmonary disease for 204 countries and territories in 2020-2050: a health-augmented macroeconomic modeling study," we provide additional details related to our study, including the mathematical formulation of our model and detailed data sources.
A: Health burden of chronic obstructive pulmonary disease Figures S1-S3 show the health burden of chronic obstructive pulmonary disease (COPD). The numbers are based on the Global Burden of Disease Study (2020). 1 *Gray areas represent countries with insufficient data This detailed model description follows our previous contributions, wherein we applied the framework to estimate the economic burden of noncommunicable diseases in China, Japan, and South Korea 2 and in the United States and European countries; 3,4 the burden of road injuries; 5 the burden of cancer; 6 and the burden of risk factors such as tobacco 7 and air pollution. 8 We aimed to quantify COPD's impact on economic output through healthcare expenditures and through productivity losses due to mortality and morbidity. For each country, we did the following analysis: Step 1. We identified the disease burden of COPD (in terms of mortality, morbidity, and treatment costs).
Step 2. We constructed economic projections for two scenarios: (i) the aggregate output in the status quo scenario without additional interventions to reduce COPD mortality and morbidity in year t and (ii) the aggregate output � in the counterfactual scenario with complete COPD elimination at zero cost in year t. The economic projections utilize a macroeconomic production function and can be further decomposed into two parts: a) Projections of effective labor supply and b) Projections of physical capital accumulation.
Step 3. We calculated the economic loss as the cumulative difference in projected annual gross domestic product (GDP) between these two scenarios:

Production function
Consider an economy in which time = 1,2, … , ∞ evolves discretely. Building upon Lucas (1988), 9 we considered the following production function for this economy: where is aggregate output; is the technological level at time , which we assume evolves exogenously; is the physical capital stock (i.e., machines, factory buildings, etc.); and represents aggregate human capital. The parameter is the elasticity of final output with respect to physical capital. Solow's framework, upon which the World Health Organization's original EPIC macroeconomic model is based, 10 only considers physical capital and raw labor as factors of output production. 11 However, the aggregate production function recognizes that output is not only produced with these factors but also with effective labor, including health, which is a crucial determinant.
Physical capital evolves according to where refers to the depreciation rate, refers to the saving rate, refers to the costs of ongoing treatment of COPD, and refers to the amount of consumption. From Equation (2), it follows that the saving rate is defined as Note that aggregate output is used for three purposes: (i) to pay treatment costs (hospitalization, medication, etc.), (ii) to consume the amount , and (iii) to save.
Individuals of age group are endowed with ℎ ( ) units of human capital and supply ℓ ( ) units of labor from age 15 up to their retirement at age , i.e., for ∈ [15, R]. Children younger than 15 and retirees older than do not work. R varies by country and could correspond to a high age (e.g., some people older than 80 could also be working). In the theoretical derivations, R indicates the upper bound of the summation. In our simulations, we used labor projections data from the International Labour Organization where positive values for the labor force exist for cohorts older than 65. Aggregate human capital in the production function (1) is then defined as the sum over the age-specific effective labor supply of each age group: where denotes the number of individuals in age group . Note that aggregate human capital increases with the number of working-age individuals who live in the economy (i.e., with a higher = ∑

( ) =15
), with individual human capital endowment (i.e., with a higher ℎ ( ) for at least one ), and with labor supply (i.e., with a higher ℓ ( ) for at least one ). This human capital function itself allows for the substitution of capital (machines or robots) for workers. In addition, if the disease predominantly affects one age group, then workers from other age groups can substitute.
We followed Mincer (1974) 12 and constructed the average human capital of the cohort aged according to an exponential function of education and work experience: where 1 is the semi-elasticity of human capital with respect to average years of education as given by ( ) , and 2 and 3 are the semi-elasticities of human capital with respect to experience of the workforce � − ( ) − 5� and experience of the workforce squared � − ( ) − 5� 2 , respectively. Here, we assumed a school entry age of 5 years throughout.

Impact of COPD on labor supply
Following Bloom et al. (2020) 2 and Chen et al. (20182 and Chen et al. ( , 2019a2 and Chen et al. ( , 20232 and Chen et al. ( , 2019b2 and Chen et al. ( , 2019c, 3,[5][6][7][8] the evolution of labor supply in the status quo scenario is given by where ( ) is the overall mortality rate of age group at time . Mortality and morbidity reduce effective labor supply.
The reduction of the population size ( ) captures the mortality effect.
Let , ( ) denote the mortality rate of people in age group due to COPD and let − , ( ) be the overall mortality rate due to causes other than COPD, then we have .
Next, we considered the mortality effect of COPD. In general, it reduces labor supply by reducing the population ( ) (through , ( ) ). In the counterfactual case, where COPD is eliminated from time = 0 onward, the evolution of labor supply is defined similarly to Equation (5), but with a different overall mortality rate ( − , ( ) instead of ( ) ). For simplicity, we assumed that the number of births is the same in both cases at each point in time .
In the counterfactual scenario, in which we denote variables with an overbar, the size of the cohort aged at time ( � ( ) ) evolves according to Following Bloom et al. (2020), 2 the loss of labor due to mortality accumulates over the years according to The reduction of the labor participation rate ℓ ( ) captures the morbidity effect because people with an illness typically reduce their labor supply, either by reducing working hours or by leaving the workforce. Following Bloom et al. (2020) 2 , the labor participation rate in the counterfactual scenario ℓ � ( ) can be calculated as where ( ) measures the size of the morbidity effect relative to the relevant mortality rate and where is the probability that a patient died from COPD by time .
Because the impact of morbidity is hard to estimate directly, we first defined Next, we assumed that the following holds in any given year for each age group : where ( ) represents the years lived with COPD and ( ) represents the years of life lost due to COPD. Notice that ( ) can be calculated from the corresponding disability-adjusted life year data reported by the Global Burden of Disease Study (2020). 1 In sum, by reducing the prevalence of COPD, the counterfactual scenario is associated with an increase in labor supply as compared with the status quo scenario. We approximated the change in labor supply (at time for age group ) by Bloom et al. (2020) 2 provide the detailed mathematical proof.

Impact of COPD on physical capital accumulation
COPD also impedes physical capital accumulation because savings finance part of the treatment costs. Following Bloom et al. (2020) 2 and Chen et al. (2018), 3 physical capital accumulation in the counterfactual scenario can be written as where is the fraction of the treatment cost that is diverted to savings. The counterfactual saving rate is thus defined by For more details, see Bloom et al. (2020) 2 and Chen et al. (2018). 3 Because COPD is assumed to be eliminated in the counterfactual scenario, the resources that were devoted to its treatment can now be used for savings or for consumption. Notice that this creates an income effect that, in reality, could affect the division of households' income between savings and consumption. For tractability, we assume that aggregate investment consists of two parts in the counterfactual scenario: a fixed share s of total output and an additional part from that would otherwise have been used to pay to treat COPD: Similarly, for the case of a partial reduction in COPD prevalence by , we have The intuition is that if COPD is partially eliminated, the treatment cost that is diverted to savings should be added back proportionally.

C: Data description Education
Age-specific educational attainment data were obtained from the Barro-Lee Educational Attainment Database, 13 which provides educational attainment data in five-year age groups up to 2010. For 2010-2030, no age-specific data are available, but the database provides projections for the population aged 15-64. We approximated the age-specific estimates by assuming that educational attainment for each age group grows at the same rate. Because the Barro-Lee database presents data in five-year intervals, linear interpolation was adopted to extend the estimates for each year.
For 2030-2050, we projected educational attainment by assuming the same growth rate as in 2010-2030 for each age and sex group.

Mortality/morbidity
Mortality and morbidity (measured in YLLs and YLDs) due to COPD up to 2019 were obtained from the recently updated Global Burden of Disease (GBD) estimates. 1 To extend the estimates beyond 2019, we assumed that the mortality rates from COPD grow at the same rate as in 2010-2019 for each country. Morbidity estimates were obtained similarly. If the projected mortality rate grew too large (i.e., if it more than doubled the current annual rate in 30 years), we limited the mortality rate's growth rate to 2%.

GDP projection
The GDP estimates (in constant 2017 international dollars or INT$) up to 2020 are from the World Bank database. 14 The GDP growth rates for 2021-2027 are from the International Monetary Fund's World Economic Outlook as of April 2022. 15 We assumed that growth beyond 2027 will be the same as in 2015-2019.

Physical capital
For each country, the physical capital stock (in 2017 INT$) was obtained from the Penn World Table projections (2021), 16 with the value for the output elasticity of physical capital (the percentage change in output for a 1% change in the physical capital stock) following standard economic estimates. 17

Labor and population projection
For each country, the labor participation rate and population estimates (by five-year age group) were obtained from the International Labour Organization database for 2015-2030. 18 For estimates beyond 2030, we assumed that growth of the labor participation rate remains the same as in 2020-2030.

Saving rate and health expenditure
We obtained country-specific saving rates and health expenditures from the World Bank database (2020b). 19 For the projection, we assumed that the saving rates remain constant (at the average in 2010-2019), while health expenditures (as a percentage of GDP) grow at the same rate as in 2000-2019.

Treatment costs
Total treatment cost for COPD in the United States is based on Dieleman et al. (2020). 20 Their total treatment cost estimate includes inpatient and outpatient medical costs due to COPD, which amount to INT$35.0 billion (34.3 billion in U.S. dollars) in 2016 (1.268% of health expenditures for all disease categories). We calculated the per case costs for the countries with data and extrapolated costs for countries without data, under the assumption that per case costs are proportional to per capita health expenditure, as was done in previous studies. 5,21,22 Specifically, we extrapolated COPD treatment cost per case using GBD disease prevalence data and extrapolated costs to other countries using a scaling factor (defined as the ratio of health expenditures per capita between the country of interest and the country with available data). We then calculated COPD treatment cost per capita by multiplying treatment cost per case by COPD prevalence. For years after 2010, we assumed that the COPD treatment cost would grow at the same rate as per capita health expenditure for each country. To make estimates among countries comparable, we convert all costs to the base year of 2017.

Discount rate
The discount rate of 3% is somewhat standard in global health and is recommended, for example, by the Panels on Cost-effectiveness in Health and Medicine. 23 However, an appropriate discount rate depends on the current economic context and varies across countries. For example, the discount rate for low-and middle-income countries should be larger than for high-income countries, e.g., 5%. 24 For country-specific discount rates, 17 of 22 national guidelines for economic evaluations recommend discount rates ranging from 1.5% to 5%. 25 Therefore, we have chosen 3% as the discount in the main analysis, and we provide the projected economic burden if discounted at 0%, 2%, 4%, and 5% in the Appendix.

Other parameter values and data sources
The dynamics of individual human capital are based on a Mincerian specification of the dependence of individual productivity on education and experience and the corresponding returns. 12 The estimated parameters for the Mincerian specification come from Psacharopoulos and Patrinos (2018) 26 for education and from Heckman et al. (2006) 27 for experience.   Table S2 describes some of the data lacking for 60 countries.

D: Imputation
For the 60 countries and territories with incomplete data (mostly on education, physical capital, and the saving rate), we used a linear projection to approximate the economic burden of COPD. Thus, we used the percentage of economic loss in total GDP from 2020-2050 as the dependent variable and disability-adjusted life years (DALYs) due to COPD in 2020-2050 as the independent variable, based on results from the 144 countries with complete data. To get the adjusted DALYs, we used the age-sex fraction of each country in 2019 as a constant age-standardized rate because we lacked some age-sex-specific population data for 2020-2050. The primary ordinary linear regression model (Model 1) is as follows: To guarantee the model's accuracy and robustness, we also considered including country GDP per capita in 2019 (in constant 2017 INT$), country life expectancy in 2019, 29 and country population size in 2019 one by one and formulated the following three models: Model 2: Model 4: Of the total 204 countries, 144 have complete data and 33 countries have data on population, GDP, and national life expectancy. These 177 countries are used for analysis in the following imputation analysis. Table S3 presents the regression results for these four models. To prevent the impact of heteroskedasticity, we rely on robust standard errors to derive more conservative estimates of the variance.  Table S2 for the specific countries with or without data.
The regression table shows that the Akaike Information Criterion supports Model 4, while the Bayes Information Criterion supports Model 1 as it penalizes the number of independent variables more severely. The total economic burden for these 177 countries is very similar. Therefore, considering interpretability and data completeness, we finally chose Model 1 as the imputation model. Figure S4 presents the diagnostic plots of Model 1. Considering the distribution of residuals, we believe that the normality of the residual assumption is reasonable except for a few outliers, but the assumption of equal variance of error terms does not seem to hold. Absolute residuals are positively correlated with DALYs. Thus, we used weighted least squares to estimate the economic loss. Table S4 shows the regression results for the weighted least squares estimation.   Table S2 for the specific countries with or without data.

Others
Cook Islands* 15 (11)(12)(13)(14)(15)(16)(17)(18)(19) 10(8-13) 7(5-9) 5(4-7) Niue* 1(0-1) 0(0-1) 0(0-0) 0(0-0) Palestine* 437(288-647) 294(192-435) 201(131-297) 167(109-247) Tokelau* 0(0-0) 0(0-0) 0(0-0) 0(0-0) *Please note that results for countries marked with an asterisk are imputed due to missing data. † Uncertainty intervals in parentheses are calculated based on the lower and upper bounds of 95% uncertainty intervals for GBD mortality and morbidity data.  7,858(6,025-10,063) 5,257(4,039-6,713) 3,576(2,753-4,553) 2,970(2,289-3,776) † Uncertainty intervals in parentheses are calculated based on the lower and upper bounds of 95% uncertainty intervals for GBD mortality and morbidity data F: Sensitivity analysis on parameters Tables S7-S8 show the total discounted economic burden of COPD in 2020-2050 for 204 countries, by country, by World Bank region, and by World Bank income group via probabilistic sensitivity analysis on parameters in Table  S1. For each country, we drew 200 random samples from the uniform distributions of each parameter within a range of 50% to 150% of the initial value and estimated the economic burden of COPD. We used the same random scaling for the three parameters in the Mincer equation because we want to maintain the relative size relationship among these three parameters. Results were aggregated to compute regional and country mean values with a 95% uncertainty interval (UI). All future year estimates were discounted using an annual discount rate of 3%.  (3,237) 114(81-160) 502(360-706) † Uncertainty intervals in parentheses are calculated based on the lower and upper bounds of 95% uncertainty intervals of the burden by varying parameters.  • The calculations are based on recently developed methods and rely on the best available global data, including data from the recently updated Global Burden of Disease Study 2019, the Barro-Lee education database, the World Bank, and the International Labour Organization. • This study is the first to account for COPD's influence on economic growth through mortality, morbidity, and the effect of treatment expenditures for 204 countries and territories. • Our framework is the first to consider economic adjustment mechanisms (substitution for labor lost through COPD and savings responses to COPD healthcare costs) explicitly in the analysis for 204 countries and territories. Lack of such consideration has been a key limitation of previous studies. • This study is the first to incorporate age-specific human capital to account for education-related productivity differences among members of different cohorts who are differentially affected by COPD for 204 countries and territories. • Our work shows the causal relationship between COPD and GDP. It avoids issues of reverse causality because we did not estimate the relationship but constructed it from our simulated production function. • We provided a detailed, step-by-step description of our methods and the data sources and specific parameters we used in our analysis in the Appendix. • We included sensitivity analyses to account for underlying uncertainty by applying varying discount rates (0%, 2%, 3%, 4%, and 5%), by adjusting the mortality and morbidity data based on the upper and lower bounds of the GBD data, and by randomly sampling model parameters (50% to 150% of the initial values). • We calculated and compared the lifetime health burden with the economic burden to show global inequalities.

Limitations
• To derive country-specific COPD health expenditure we had to extrapolate COPD-related treatment costs for most countries without direct data under the assumption that per case costs are proportional to per capita health expenditure. Specifically, we calculated COPD treatment costs per case for the United States, which are then projected to other countries based on GBD disease prevalence data and health expenditure per capita. This could either underestimate or overestimate country-specific treatment costs for COPD. • Due to missing data, we had to impute the economic burden of COPD for 60 out of the 204 countries and territories. However, this does not significantly compromise our results, given that the 144 countries for which we had complete data account for 93% of global population and 96% of global GDP. • We did not include behavioural changes-such as changing labour force participation-among family members who might need to care for patients with COPD. Thus, along this dimension, our findings provide a lower bound for the economic costs of COPD because we did not account for the full costs of patient care. 30

I: CHEERS guideline
We also included the Consolidated Health Economic Evaluation Reporting Standards 2022 (CHEERS 2022) statement 31 as follows.

Section Topic/Checklist item Item No
Guidance for reporting

Title
Title 1

Introduction
Background and objectives 3 Give the context for the study, the study question, and its practical relevance for decision making in policy or practice. Describe any approaches to engage patients or service recipients, the general public, communities, or stakeholders (such as clinicians or payers) in the design of the study.

Study parameters 22
Report all analytic inputs (such as values, ranges, references) including uncertainty or distributional assumptions.